OpenRaiser/Intern-Atlas huggingface dataset:
Multi-LLM Collaboration + Data-Centric Innovation = 2x Better Vulnerability Repair
Venue: arXiv cs.SE 2024
Year: 2024

The advances of deep learning (DL) have paved the way for automatic software vulnerability repair approaches, which effectively learn the mapping from the vulnerable code to the fixed code. Nevertheless, existing DL-based vulnerability repair methods face notable limitations: 1) they struggle to handle lengthy vulnerable code, 2) they treat code as natural language texts, neglecting its inherent structure, and 3) they do not tap into the valuable expert knowledge present in the expert system. To address this, we propose VulMaster, a Transformer-based neural network model that excels at generating vulnerability repairs through data-centric innovation. Specifically, VulMaster introduces the utilization and combination of various types of input data, including complete vulnerable code of any size, vulnerable code structures, and expert knowledge from the CWE system. Additionally, VulMaster leverages the collaboration between two Large Language Models (LLMs), CodeT5 and ChatGPT: CodeT5 acts as the customizable backbone LLM, fine-tuned with the training data, while ChatGPT supplements by providing missing relevant inputs to CodeT5. We evaluated VulMaster on a real-world C/C++ vulnerability repair dataset comprising 1,754 projects with 5,800 vulnerable functions. The experimental results demonstrated that VulMaster exhibits substantial improvements compared to the learning-based state-of-the-art vulnerability repair approach. Specifically, VulMaster improves the EM, BLEU, and CodeBLEU scores from 10.2\% to 20.0\%, 21.3\% to 29.3\%, and 32.5\% to 40.9\%, respectively.
Navigating the Post-API Dilemma | Search Engine Results Pages Present a Biased View of Social Media Data
Venue: arXiv cs.IR 2024
Year: 2024

Recent decisions to discontinue access to social media APIs are having detrimental effects on Internet research and the field of computational social science as a whole. This lack of access to data has been dubbed the Post-API era of Internet research. Fortunately, popular search engines have the means to crawl, capture, and surface social media data on their Search Engine Results Pages (SERP) if provided the proper search query, and may provide a solution to this dilemma. In the present work we ask: does SERP provide a complete and unbiased sample of social media data? Is SERP a viable alternative to direct API-access? To answer these questions, we perform a comparative analysis between (Google) SERP results and nonsampled data from Reddit and Twitter/X. We find that SERP results are highly biased in favor of popular posts; against political, pornographic, and vulgar posts; are more positive in their sentiment; and have large topical gaps. Overall, we conclude that SERP is not a viable alternative to social media API access.
Style-News: Incorporating Stylized News Generation and Adversarial Verification for Neural Fake News Detection
Venue: arXiv cs.CL 2024
Year: 2024

With the improvements in generative models, the issues of producing hallucinations in various domains (e.g., law, writing) have been brought to people's attention due to concerns about misinformation. In this paper, we focus on neural fake news, which refers to content generated by neural networks aiming to mimic the style of real news to deceive people. To prevent harmful disinformation spreading fallaciously from malicious social media (e.g., content farms), we propose a novel verification framework, Style-News, using publisher metadata to imply a publisher's template with the corresponding text types, political stance, and credibility. Based on threat modeling aspects, a style-aware neural news generator is introduced as an adversary for generating news content conditioning for a specific publisher, and style and source discriminators are trained to defend against this attack by identifying which publisher the style corresponds with, and discriminating whether the source of the given news is human-written or machine-generated. To evaluate the quality of the generated content, we integrate various dimensional metrics (language fluency, content preservation, and style adherence) and demonstrate that Style-News significantly outperforms the previous approaches by a margin of 0.35 for fluency, 15.24 for content, and 0.38 for style at most. Moreover, our discriminative model outperforms state-of-the-art baselines in terms of publisher prediction (up to 4.64%) and neural fake news detection (+6.94% $\sim$ 31.72%).
DocuBits: VR Document Decomposition for Procedural Task Completion
Venue: arXiv cs.HC 2024
Year: 2024

Reading monolithic instructional documents in VR is often challenging, especially when tasks are collaborative. Here we present DocuBits, a novel method for transforming monolithic documents into small, interactive instructional elements. Our approach allows users to:(i) create instructional elements (ii) position them within VR and (iii) use them to monitor and share progress in a multi-user VR learning environment. We describe our design methodology as well as two user studies evaluating how both individual users and pairs of users interact with DocuBits compared to monolithic documents while performing a chemistry lab task. Our analysis shows that, for both studies, DocuBits had substantially higher usability, while decreasing perceived workload (p < 0.001$. Our collaborative study showed that participants perceived higher social presence, collaborator awareness as well as immersion and presence (p < 0.001). We discuss our insights for using text-based instructions to support enhanced collaboration in VR environments.
Oracle-Efficient Hybrid Online Learning with Unknown Distribution
Venue: arXiv cs.LG 2024
Year: 2024

We study the problem of oracle-efficient hybrid online learning when the features are generated by an unknown i.i.d. process and the labels are generated adversarially. Assuming access to an (offline) ERM oracle, we show that there exists a computationally efficient online predictor that achieves a regret upper bounded by $\tilde{O}(T^{\frac{3}{4}})$ for a finite-VC class, and upper bounded by $\tilde{O}(T^{\frac{p+1}{p+2}})$ for a class with $α$ fat-shattering dimension $α^{-p}$. This provides the first known oracle-efficient sublinear regret bounds for hybrid online learning with an unknown feature generation process. In particular, it confirms a conjecture of Lazaric and Munos (JCSS 2012). We then extend our result to the scenario of shifting distributions with $K$ changes, yielding a regret of order $\tilde{O}(T^{\frac{4}{5}}K^{\frac{1}{5}})$. Finally, we establish a regret of $\tilde{O}((K^{\frac{2}{3}}(\log|\mathcal{H}|)^{\frac{1}{3}}+K)\cdot T^{\frac{4}{5}})$ for the contextual $K$-armed bandits with a finite policy set $\mathcal{H}$, i.i.d. generated contexts from an unknown distribution, and adversarially generated costs.
Exploring the Transferability of a Foundation Model for Fundus Images: Application to Hypertensive Retinopathy
Venue: arXiv cs.CV 2024
Year: 2024

Using deep learning models pre-trained on Imagenet is the traditional solution for medical image classification to deal with data scarcity. Nevertheless, relevant literature supports that this strategy may offer limited gains due to the high dissimilarity between domains. Currently, the paradigm of adapting domain-specialized foundation models is proving to be a promising alternative. However, how to perform such knowledge transfer, and the benefits and limitations it presents, are under study. The CGI-HRDC challenge for Hypertensive Retinopathy diagnosis on fundus images introduces an appealing opportunity to evaluate the transferability of a recently released vision-language foundation model of the retina, FLAIR. In this work, we explore the potential of using FLAIR features as starting point for fundus image classification, and we compare its performance with regard to Imagenet initialization on two popular transfer learning methods: Linear Probing (LP) and Fine-Tuning (FP). Our empirical observations suggest that, in any case, the use of the traditional strategy provides performance gains. In contrast, direct transferability from FLAIR model allows gains of 2.5%. When fine-tuning the whole network, the performance gap increases up to 4%. In this case, we show that avoiding feature deterioration via LP initialization of the classifier allows the best re-use of the rich pre-trained features. Although direct transferability using LP still offers limited performance, we believe that foundation models such as FLAIR will drive the evolution of deep-learning-based fundus image analysis.
CRYSTALS-Kyber With Lattice Quantizer
Venue: arXiv cs.IT 2024
Year: 2024

Module Learning with Errors (M-LWE) based key reconciliation mechanisms (KRM) can be viewed as quantizing an M-LWE sample according to a lattice codebook. This paper describes a generic M-LWE-based KRM framework, valid for any dimensional lattices and any modulus $q$ without a dither. Our main result is an explicit upper bound on the decryption failure rate (DFR) of M-LWE-based KRM. This bound allows us to construct optimal lattice quantizers to reduce the DFR and communication cost simultaneously. Moreover, we present a KRM scheme using the same security parameters $(q,k,η_1,η_2)$ as in Kyber. Compared with Kyber, the communication cost is reduced by up to $36.47\%$ and the DFR is reduced by a factor of up to $2^{99}$. The security arguments remain the same as Kyber.
Quantifying Stereotypes in Language
Venue: arXiv cs.CL 2024
Year: 2024

A stereotype is a generalized perception of a specific group of humans. It is often potentially encoded in human language, which is more common in texts on social issues. Previous works simply define a sentence as stereotypical and anti-stereotypical. However, the stereotype of a sentence may require fine-grained quantification. In this paper, to fill this gap, we quantify stereotypes in language by annotating a dataset. We use the pre-trained language models (PLMs) to learn this dataset to predict stereotypes of sentences. Then, we discuss stereotypes about common social issues such as hate speech, sexism, sentiments, and disadvantaged and advantaged groups. We demonstrate the connections and differences between stereotypes and common social issues, and all four studies validate the general findings of the current studies. In addition, our work suggests that fine-grained stereotype scores are a highly relevant and competitive dimension for research on social issues.
PPM: Automated Generation of Diverse Programming Problems for Benchmarking Code Generation Models
Venue: arXiv cs.SE 2024
Year: 2024

In recent times, a plethora of Large Code Generation Models (LCGMs) have been proposed, showcasing significant potential in assisting developers with complex programming tasks. Benchmarking LCGMs necessitates the creation of a set of diverse programming problems, and each problem comprises the prompt (including the task description), canonical solution, and test inputs. The existing methods for constructing such a problem set can be categorized into two main types: manual methods and perturbation-based methods. However, manual methods demand high effort and lack scalability, while also risking data integrity due to LCGMs' potentially contaminated data collection, and perturbation-based approaches mainly generate semantically homogeneous problems with the same canonical solutions and introduce typos that can be easily auto-corrected by IDE, making them ineffective and unrealistic. In this work, we propose the idea of programming problem merging (PPM) and provide two implementation of this idea, we utilize our tool on two widely-used datasets and compare it against nine baseline methods using eight code generation models. The results demonstrate the effectiveness of our tool in generating more challenging, diverse, and natural programming problems, comparing to the baselines.
numaPTE: Managing Page-Tables and TLBs on NUMA Systems
Venue: arXiv cs.OS 2024
Year: 2024

Memory management operations that modify page-tables, typically performed during memory allocation/deallocation, are infamous for their poor performance in highly threaded applications, largely due to process-wide TLB shootdowns that the OS must issue due to the lack of hardware support for TLB coherence. We study these operations in NUMA settings, where we observe up to 40x overhead for basic operations such as munmap or mprotect. The overhead further increases if page-table replication is used, where complete coherent copies of the page-tables are maintained across all NUMA nodes. While eager system-wide replication is extremely effective at localizing page-table reads during address translation, we find that it creates additional penalties upon any page-table changes due to the need to maintain all replicas coherent. In this paper, we propose a novel page-table management mechanism, called numaPTE, to enable transparent, on-demand, and partial page-table replication across NUMA nodes in order to perform address translation locally, while avoiding the overheads and scalability issues of system-wide full page-table replication. We then show that numaPTE's precise knowledge of page-table sharers can be leveraged to significantly reduce the number of TLB shootdowns issued upon any memory-management operation. As a result, numaPTE not only avoids replication-related slowdowns, but also provides significant speedup over the baseline on memory allocation/deallocation and access control operations. We implement numaPTEin Linux on x86_64, evaluate it on 4- and 8-socket systems, and show that numaPTE achieves the full benefits of eager page-table replication on a wide range of applications, while also achieving a 12% and 36% runtime improvement on Webserver and Memcached respectively due to a significant reduction in TLB shootdowns.
An Analysis of Letter Dynamics in the English Alphabet
Venue: arXiv cs.IT 2024
Year: 2024

The frequency with which the letters of the English alphabet appear in writings has been applied to the field of cryptography, the development of keyboard mechanics, and the study of linguistics. We expanded on the statistical analysis of the English alphabet by examining the average frequency which each letter appears in different categories of writings. We evaluated news articles, novels, plays, scientific publications and calculated the frequency of each letter of the alphabet, the information density of each letter, and the overall letter distribution. Furthermore, we developed a metric known as distance, d that can be used to algorithmically recognize different categories of writings. The results of our study can be applied to information transmission, large data curation, and linguistics.
BrepGen: A B-rep Generative Diffusion Model with Structured Latent Geometry
Venue: arXiv cs.CV 2024
Year: 2024

This paper presents BrepGen, a diffusion-based generative approach that directly outputs a Boundary representation (B-rep) Computer-Aided Design (CAD) model. BrepGen represents a B-rep model as a novel structured latent geometry in a hierarchical tree. With the root node representing a whole CAD solid, each element of a B-rep model (i.e., a face, an edge, or a vertex) progressively turns into a child-node from top to bottom. B-rep geometry information goes into the nodes as the global bounding box of each primitive along with a latent code describing the local geometric shape. The B-rep topology information is implicitly represented by node duplication. When two faces share an edge, the edge curve will appear twice in the tree, and a T-junction vertex with three incident edges appears six times in the tree with identical node features. Starting from the root and progressing to the leaf, BrepGen employs Transformer-based diffusion models to sequentially denoise node features while duplicated nodes are detected and merged, recovering the B-Rep topology information. Extensive experiments show that BrepGen advances the task of CAD B-rep generation, surpassing existing methods on various benchmarks. Results on our newly collected furniture dataset further showcase its exceptional capability in generating complicated geometry. While previous methods were limited to generating simple prismatic shapes, BrepGen incorporates free-form and doubly-curved surfaces for the first time. Additional applications of BrepGen include CAD autocomplete and design interpolation. The code, pretrained models, and dataset are available at https://github.com/samxuxiang/BrepGen.
Evaluating Gender Bias in Large Language Models via Chain-of-Thought Prompting
Venue: arXiv cs.CL 2024
Year: 2024

There exist both scalable tasks, like reading comprehension and fact-checking, where model performance improves with model size, and unscalable tasks, like arithmetic reasoning and symbolic reasoning, where model performance does not necessarily improve with model size. Large language models (LLMs) equipped with Chain-of-Thought (CoT) prompting are able to make accurate incremental predictions even on unscalable tasks. Unfortunately, despite their exceptional reasoning abilities, LLMs tend to internalize and reproduce discriminatory societal biases. Whether CoT can provide discriminatory or egalitarian rationalizations for the implicit information in unscalable tasks remains an open question. In this study, we examine the impact of LLMs' step-by-step predictions on gender bias in unscalable tasks. For this purpose, we construct a benchmark for an unscalable task where the LLM is given a list of words comprising feminine, masculine, and gendered occupational words, and is required to count the number of feminine and masculine words. In our CoT prompts, we require the LLM to explicitly indicate whether each word in the word list is a feminine or masculine before making the final predictions. With counting and handling the meaning of words, this benchmark has characteristics of both arithmetic reasoning and symbolic reasoning. Experimental results in English show that without step-by-step prediction, most LLMs make socially biased predictions, despite the task being as simple as counting words. Interestingly, CoT prompting reduces this unconscious social bias in LLMs and encourages fair predictions.
OpineBot: Class Feedback Reimagined Using a Conversational LLM
Venue: arXiv cs.HC 2024
Year: 2024

Conventional class feedback systems often fall short, relying on static, unengaging surveys offering little incentive for student participation. To address this, we present OpineBot, a novel system employing large language models (LLMs) to conduct personalized, conversational class feedback via chatbot interface. We assessed OpineBot's effectiveness in a user study with 20 students from an Indian university's Operating-Systems class, utilizing surveys and interviews to analyze their experiences. Findings revealed a resounding preference for OpineBot compared to conventional methods, highlighting its ability to engage students, produce deeper feedback, offering a dynamic survey experience. This research represents a work in progress, providing early results, marking a significant step towards revolutionizing class feedback through LLM-based technology, promoting student engagement, and leading to richer data for instructors. This ongoing research presents preliminary findings and marks a notable advancement in transforming classroom feedback using LLM-based technology to enhance student engagement and generate comprehensive data for educators.
Comuniqa : Exploring Large Language Models for improving speaking skills
Venue: arXiv cs.HC 2024
Year: 2024

In this paper, we investigate the potential of Large Language Models (LLMs) to improve English speaking skills. This is particularly relevant in countries like India, where English is crucial for academic, professional, and personal communication but remains a non-native language for many. Traditional methods for enhancing speaking skills often rely on human experts, which can be limited in terms of scalability, accessibility, and affordability. Recent advancements in Artificial Intelligence (AI) offer promising solutions to overcome these limitations. We propose Comuniqa, a novel LLM-based system designed to enhance English speaking skills. We adopt a human-centric evaluation approach, comparing Comuniqa with the feedback and instructions provided by human experts. In our evaluation, we divide the participants in three groups: those who use LLM-based system for improving speaking skills, those guided by human experts for the same task and those who utilize both the LLM-based system as well as the human experts. Using surveys, interviews, and actual study sessions, we provide a detailed perspective on the effectiveness of different learning modalities. Our preliminary findings suggest that while LLM-based systems have commendable accuracy, they lack human-level cognitive capabilities, both in terms of accuracy and empathy. Nevertheless, Comuniqa represents a significant step towards achieving Sustainable Development Goal 4: Quality Education by providing a valuable learning tool for individuals who may not have access to human experts for improving their speaking skills.
FreeStyle: Free Lunch for Text-guided Style Transfer using Diffusion Models
Venue: arXiv cs.CV 2024
Year: 2024

The rapid development of generative diffusion models has significantly advanced the field of style transfer. However, most current style transfer methods based on diffusion models typically involve a slow iterative optimization process, e.g., model fine-tuning and textual inversion of style concept. In this paper, we introduce FreeStyle, an innovative style transfer method built upon a pre-trained large diffusion model, requiring no further optimization. Besides, our method enables style transfer only through a text description of the desired style, eliminating the necessity of style images. Specifically, we propose a dual-stream encoder and single-stream decoder architecture, replacing the conventional U-Net in diffusion models. In the dual-stream encoder, two distinct branches take the content image and style text prompt as inputs, achieving content and style decoupling. In the decoder, we further modulate features from the dual streams based on a given content image and the corresponding style text prompt for precise style transfer. Our experimental results demonstrate high-quality synthesis and fidelity of our method across various content images and style text prompts. Compared with state-of-the-art methods that require training, our FreeStyle approach notably reduces the computational burden by thousands of iterations, while achieving comparable or superior performance across multiple evaluation metrics including CLIP Aesthetic Score, CLIP Score, and Preference. We have released the code at: https://github.com/FreeStyleFreeLunch/FreeStyle.